Motion Estimation in Video Sequences

In this paper, we will perform motion estimation on video sequences. This process involves analyzing pixel-level displacements between consecutive frames to better understand moving targets within the video. We will utilize optical flow algorithms (such as Lucas-Kanade or Horn-Schunck methods) to calculate motion vectors, which represent the direction and magnitude of pixel movements. Furthermore, we will segment the moving targets from the background using motion-based segmentation techniques. This typically involves applying motion-compensated frame differencing or background subtraction algorithms, combined with morphological operations to remove noise and fill gaps in the detected objects. The segmentation process enables better identification and tracking of these moving objects. This approach allows us to deeply investigate the motion patterns and behavioral characteristics of moving targets. Additionally, we will explore how to utilize motion information from these targets for more accurate object tracking and trajectory prediction. This may involve implementing Kalman filters or particle filters to handle occlusions and predict future positions. Through these methodologies, we can achieve enhanced understanding and analysis of moving targets in videos, laying a stronger foundation for future video analysis and processing applications. The implementation typically involves frame preprocessing, motion vector calculation, object segmentation, and tracking algorithm integration in a systematic pipeline.